C-reactive protein as a marker of postoperative complication of emergency colorectal surgery

Introduction: The literature converges regarding the use of C-reactive protein (CRP) tests between postoperative days (PODs) 3 and 5 of elective procedures. In this period, they have great sensitivity and negative predictive value (NPV) for severe and anastomotic complications about two days before the first clinical sign. The few studies on colorectal urgency suggest that, despite the different initial values according to the surgical indication, following POD 3, the level of CRP is similar to that of elective procedures. However, given the heterogeneity of the studies, there is no consensus on the cutoff values for this use. Objective: To validate the use and propose a PO CRP cut-off value in urgent colorectal procedures as an exclusion criterion for complications of anastomosis or the abdominal cavity. Method: Retrospective analysis of the medical records of 308 patients who underwent urgent colorectal surgical procedures between January 2017 and December 2019. The following data were considered: age, gender, surgical indication, type of procedure performed, complications, CRP levels preoperatively and from POD 1 to 4, and the severity of the complications. We compared the CRP levels and the percentage variations between the preoperative period and PODs 1 to 4 as markers of severe complications using the receiver operating characteristic (ROC) curve. Results: The levels of CRP on POD4, and their percentage drops between PODs 2 to 4 and PODs 3 to 4, were better to predict severe complications. A cutoff of 7.45mg/dL on POD 4 had 91.7% of sensitivity and NPV. A 50% drop between PODs 3 and 4 had 100% of sensitivity and NPV. Conclusion: Determining the level of CRP is useful to exclude severe complications, and it could be a criterion for hospital discharge in POD 4 of emergency colorectal surgery. (AU)

Machine learning and glioma imaging biomarkers.

AIM: To review how machine learning (ML) is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis, and treatment response monitoring. MATERIALS AND METHODS: The PubMed and MEDLINE databases were searched for articles published before September 2018 using relevant search terms. The search strategy focused on articles applying ML to high-grade glioma biomarkers for treatment response monitoring, prognosis, and prediction. RESULTS: Magnetic resonance imaging (MRI) is typically used throughout the patient pathway because routine structural imaging provides detailed anatomical and pathological information and advanced techniques provide additional physiological detail. Using carefully chosen image features, ML is frequently used to allow accurate classification in a variety of scenarios. Rather than being chosen by human selection, ML also enables image features to be identified by an algorithm. Much research is applied to determining molecular profiles, histological tumour grade, and prognosis using MRI images acquired at the time that patients first present with a brain tumour. Differentiating a treatment response from a post-treatment-related effect using imaging is clinically important and also an area of active study (described here in one of two Special Issue publications dedicated to the application of ML in glioma imaging). CONCLUSION: Although pioneering, most of the evidence is of a low level, having been obtained retrospectively and in single centres. Studies applying ML to build neuro-oncology monitoring biomarker models have yet to show an overall advantage over those using traditional statistical methods. Development and validation of ML models applied to neuro-oncology require large, well-annotated datasets, and therefore multidisciplinary and multi-centre collaborations are necessary.